Algovera is a maturing ecosystem of independent AI teams. So far, we have completed two rounds of grant funding, which attracted a total of nineteen proposals. Twelve out of the nineteen proposals received funding. Squads have made some exciting progress over the last few months with few projects are reaching the stage where they are starting to apply for larger funding programmes. For example, the Virtual Object Detector Squad is applying for OceanDAO funding, the DeFi Squad has a working app (video demo, code) and the Healthcare Squad produced a first draft at a whitepaper.
7 posts tagged with "Startup"
View All TagsIntroducing Algovera Reputation NFTs
As we continue to refine and improve internal processes at Algovera, we have begun to leverage the power and flexibility of NFTs to improve our Grant voting process. The Algovera Reputation NFT collection is meant to facilitate voting in Algovera Grants Round 2, where each NFT will award voting power to community members based on the criteria listed below. The collection has been minted on Polygon and are available on OpenSea, check them out here!
Update and Next Steps for Algovera Squads
Recently, we announced the launch of Algovera Grants to fund projects that combine AI and Web3.
Grant Recipients for Algovera Grants R1
Announcing Algovera’s Partnership with nCight to develop a medical image classification algorithm
Algovera’s Mission
The mission of Algovera is to empower data scientists to work independently outside of centralised tech companies. We think this is preferable to the current status quo for two main reasons.
Using the Ocean Marketplace with HuggingFace Apps, Algorithms and Datasets
HuggingFace is an online community of data scientists with a mission of making it as easy as possible to to train, optimize, and deploy models. HuggingFace Hub aims to provide a central place for collecting models, datasets and metrics. The model hub offers thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. HuggingFace Spaces provides a simple way for data scientists and organisations to demonstrate machine learning apps. It provides access to cloud compute and accelerated deployment.
Onboarding AI Startups from Web2 to Web3
The community of AI startups in Ireland is one that we’re very close with, from our time of working on our own startup developing machine learning (ML) algorithms for motion analysis in physiotherapy applications. During our experience in the space, we have spoken to and developed relationships with a large network of other startups that are developing ML and computer vision technology. Like our own previous venture, these startups follow Web2 practices. In a previous blog post, the benefits that Web3 technologies can offer in the development of AI algorithms for our use case were explored, along with our development efforts in this direction.